Overview

Dataset statistics

Number of variables31
Number of observations7003
Missing cells13921
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory725.0 B

Variable types

Numeric19
Categorical10
Boolean1
Unsupported1

Alerts

Tiglon has constant value "False" Constant
Warbler has a high cardinality: 83 distinct values High cardinality
Tern is highly correlated with TiglonHigh correlation
Thrush is highly correlated with TiglonHigh correlation
Tick is highly correlated with Tortoise and 4 other fieldsHigh correlation
Tortoise is highly correlated with Tick and 4 other fieldsHigh correlation
Trout is highly correlated with Tick and 4 other fieldsHigh correlation
Tuna is highly correlated with Urial and 2 other fieldsHigh correlation
Turkey is highly correlated with Viper and 3 other fieldsHigh correlation
Turtle is highly correlated with TiglonHigh correlation
Tyrannosaurus is highly correlated with TiglonHigh correlation
Viper is highly correlated with Turkey and 3 other fieldsHigh correlation
Vole is highly correlated with Tiger and 5 other fieldsHigh correlation
Wallaby is highly correlated with Vole and 3 other fieldsHigh correlation
Walrus is highly correlated with Vulture and 2 other fieldsHigh correlation
Wasp is highly correlated with targetHigh correlation
Whale is highly correlated with Tick and 7 other fieldsHigh correlation
Whippet is highly correlated with TiglonHigh correlation
Whitefish is highly correlated with TiglonHigh correlation
Wildcat is highly correlated with TiglonHigh correlation
Wildebeest is highly correlated with TiglonHigh correlation
Tiger is highly correlated with Vole and 1 other fieldsHigh correlation
Tiglon is highly correlated with Wolf and 9 other fieldsHigh correlation
Toad is highly correlated with Vole and 2 other fieldsHigh correlation
Urial is highly correlated with Tuna and 1 other fieldsHigh correlation
Vulture is highly correlated with Turkey and 4 other fieldsHigh correlation
Warbler is highly correlated with Tuna and 5 other fieldsHigh correlation
Weasel is highly correlated with Tick and 6 other fieldsHigh correlation
Wildfowl is highly correlated with WhaleHigh correlation
Wolf is highly correlated with TiglonHigh correlation
Wolverine is highly correlated with Tick and 11 other fieldsHigh correlation
target is highly correlated with Tuna and 1 other fieldsHigh correlation
Tiglon has 3386 (48.4%) missing values Missing
Vicuna has 7003 (100.0%) missing values Missing
Wallaby has 3532 (50.4%) missing values Missing
Wasp has unique values Unique
Vicuna is an unsupported type, check if it needs cleaning or further analysis Unsupported
Thrush has 445 (6.4%) zeros Zeros

Reproduction

Analysis started2022-11-21 03:31:08.268326
Analysis finished2022-11-21 03:31:28.617047
Duration20.35 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

Tern
Real number (ℝ)

HIGH CORRELATION

Distinct7000
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-64560.65648
Minimum-377324.5416
Maximum870769.5913
Zeros0
Zeros (%)0.0%
Negative4834
Negative (%)69.0%
Memory size54.8 KiB
2022-11-20T22:31:28.653883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-377324.5416
5-th percentile-303905.0967
Q1-194772.8398
median-90079.79128
Q335542.72171
95-th percentile265452.1433
Maximum870769.5913
Range1248094.133
Interquartile range (IQR)230315.5615

Descriptive statistics

Standard deviation175304.4827
Coefficient of variation (CV)-2.715345417
Kurtosis0.9408387517
Mean-64560.65648
Median Absolute Deviation (MAD)113543.2128
Skewness0.8588319908
Sum-452118277.3
Variance3.073166165 × 1010
MonotonicityNot monotonic
2022-11-20T22:31:28.703841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-348050.54074
 
0.1%
-253077.77241
 
< 0.1%
-221276.9391
 
< 0.1%
140981.56331
 
< 0.1%
-21675.933191
 
< 0.1%
-346753.5471
 
< 0.1%
-212202.90711
 
< 0.1%
315666.54941
 
< 0.1%
-100707.70891
 
< 0.1%
226518.59871
 
< 0.1%
Other values (6990)6990
99.8%
ValueCountFrequency (%)
-377324.54161
< 0.1%
-377324.53511
< 0.1%
-375256.74351
< 0.1%
-375066.36611
< 0.1%
-374590.3761
< 0.1%
-373732.77721
< 0.1%
-373576.66671
< 0.1%
-371732.26561
< 0.1%
-371562.04431
< 0.1%
-370741.72681
< 0.1%
ValueCountFrequency (%)
870769.59131
< 0.1%
852705.66871
< 0.1%
808191.35321
< 0.1%
804507.01491
< 0.1%
754747.14151
< 0.1%
754267.4231
< 0.1%
702714.36751
< 0.1%
671771.32241
< 0.1%
657706.0861
< 0.1%
656730.1941
< 0.1%

Thrush
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct330
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-45.0243559
Minimum-49.65924583
Maximum0
Zeros445
Zeros (%)6.4%
Negative6558
Negative (%)93.6%
Memory size54.8 KiB
2022-11-20T22:31:28.750386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-49.65924583
5-th percentile-49.65924583
Q1-49.65924583
median-49.65924583
Q3-49.65924583
95-th percentile0
Maximum0
Range49.65924583
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.37923082
Coefficient of variation (CV)-0.3193656086
Kurtosis5.842216686
Mean-45.0243559
Median Absolute Deviation (MAD)0
Skewness2.796227298
Sum-315305.5643
Variance206.762279
MonotonicityNot monotonic
2022-11-20T22:31:28.795865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-49.659245836217
88.8%
0445
 
6.4%
-7.105427358 × 10-158
 
0.1%
-2.131628207 × 10-144
 
0.1%
-49.659245832
 
< 0.1%
-5.684341886 × 10-142
 
< 0.1%
-3.552713679 × 10-142
 
< 0.1%
-49.659245831
 
< 0.1%
-22.455936741
 
< 0.1%
-0.014540167341
 
< 0.1%
Other values (320)320
 
4.6%
ValueCountFrequency (%)
-49.659245836217
88.8%
-49.659245831
 
< 0.1%
-49.659245832
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
-49.659245831
 
< 0.1%
ValueCountFrequency (%)
0445
6.4%
-7.105427358 × 10-158
 
0.1%
-2.131628207 × 10-144
 
0.1%
-3.552713679 × 10-142
 
< 0.1%
-4.97379915 × 10-141
 
< 0.1%
-5.684341886 × 10-142
 
< 0.1%
-8.526512829 × 10-141
 
< 0.1%
-1.207922651 × 10-131
 
< 0.1%
-1.634248292 × 10-131
 
< 0.1%
-1.705302566 × 10-131
 
< 0.1%

Tick
Real number (ℝ)

HIGH CORRELATION

Distinct4378
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8543212657
Minimum-1.383081229
Maximum-0.0402810057
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:28.840126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.383081229
5-th percentile-1.337000432
Q1-1.125735286
median-0.8825665979
Q3-0.6025404095
95-th percentile-0.296813541
Maximum-0.0402810057
Range1.342800223
Interquartile range (IQR)0.5231948764

Descriptive statistics

Standard deviation0.3236842678
Coefficient of variation (CV)-0.3788788607
Kurtosis-0.9005868038
Mean-0.8543212657
Median Absolute Deviation (MAD)0.2595678947
Skewness0.2687960861
Sum-5982.811824
Variance0.1047715052
MonotonicityNot monotonic
2022-11-20T22:31:28.886944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.99554172967
 
0.1%
-0.90795149916
 
0.1%
-1.1815238186
 
0.1%
-0.47345743876
 
0.1%
-0.68769420616
 
0.1%
-0.78668801586
 
0.1%
-0.38472636656
 
0.1%
-0.79762149096
 
0.1%
-0.49840479016
 
0.1%
-1.186342926
 
0.1%
Other values (4368)6942
99.1%
ValueCountFrequency (%)
-1.3830812291
< 0.1%
-1.383075611
< 0.1%
-1.3829588511
< 0.1%
-1.3827215881
< 0.1%
-1.3826805171
< 0.1%
-1.3826482581
< 0.1%
-1.3825621461
< 0.1%
-1.3824876371
< 0.1%
-1.3821875191
< 0.1%
-1.3816823461
< 0.1%
ValueCountFrequency (%)
-0.04028100571
< 0.1%
-0.040891369431
< 0.1%
-0.042721628121
< 0.1%
-0.048509208971
< 0.1%
-0.049726051421
< 0.1%
-0.057621999131
< 0.1%
-0.060046803981
< 0.1%
-0.063074688161
< 0.1%
-0.0639823771
< 0.1%
-0.066401354331
< 0.1%

Tiger
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
s
6456 
v
 
521
e
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rows
2nd rows
3rd rows
4th rows
5th rows

Common Values

ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Length

2022-11-20T22:31:28.929325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:28.964425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Most occurring characters

ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s6456
92.2%
v521
 
7.4%
e26
 
0.4%

Tiglon
Boolean

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing3386
Missing (%)48.4%
Memory size219.0 KiB
False
3617 
(Missing)
3386 
ValueCountFrequency (%)
False3617
51.6%
(Missing)3386
48.4%
2022-11-20T22:31:28.995081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Toad
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
l
4137 
s
1476 
u
799 
z
 
355
w
 
178
Other values (2)
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowl
2nd rowl
3rd rowl
4th rowl
5th rowl

Common Values

ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Length

2022-11-20T22:31:29.023077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:29.061816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l4137
59.1%
s1476
 
21.1%
u799
 
11.4%
z355
 
5.1%
w178
 
2.5%
i48
 
0.7%
o10
 
0.1%

Tortoise
Real number (ℝ)

HIGH CORRELATION

Distinct3426
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-16.80491886
Minimum-32.2664759
Maximum-0.03010166289
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:29.102616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-32.2664759
5-th percentile-28.82874191
Q1-22.48101147
median-16.84687548
Q3-11.05007844
95-th percentile-5.165325088
Maximum-0.03010166289
Range32.23637424
Interquartile range (IQR)11.43093303

Descriptive statistics

Standard deviation7.274371337
Coefficient of variation (CV)-0.4328715536
Kurtosis-0.8800124844
Mean-16.80491886
Median Absolute Deviation (MAD)5.694838008
Skewness-0.02274872709
Sum-117684.8468
Variance52.91647834
MonotonicityNot monotonic
2022-11-20T22:31:29.144949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.103686828
 
0.1%
-14.724863948
 
0.1%
-9.8117826067
 
0.1%
-17.546427177
 
0.1%
-11.55969027
 
0.1%
-13.749679637
 
0.1%
-10.145428697
 
0.1%
-17.970390727
 
0.1%
-27.271071747
 
0.1%
-12.276960597
 
0.1%
Other values (3416)6931
99.0%
ValueCountFrequency (%)
-32.26647591
< 0.1%
-32.252462641
< 0.1%
-32.249611761
< 0.1%
-32.244970591
< 0.1%
-32.233357911
< 0.1%
-32.221523581
< 0.1%
-32.219247871
< 0.1%
-32.217699521
< 0.1%
-32.214527882
< 0.1%
-32.209160921
< 0.1%
ValueCountFrequency (%)
-0.030101662891
< 0.1%
-0.080239884151
< 0.1%
-0.11030408681
< 0.1%
-0.15036783851
< 0.1%
-0.20041240911
< 0.1%
-0.46997880561
< 0.1%
-0.49986038941
< 0.1%
-0.75824567771
< 0.1%
-0.77807776551
< 0.1%
-0.79790360992
< 0.1%

Trout
Real number (ℝ)

HIGH CORRELATION

Distinct4760
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-451.0483254
Minimum-576.5558259
Maximum-343.839588
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:29.188651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-576.5558259
5-th percentile-529.7393057
Q1-481.8227021
median-445.7251829
Q3-418.775842
95-th percentile-386.1882394
Maximum-343.839588
Range232.7162379
Interquartile range (IQR)63.04686008

Descriptive statistics

Standard deviation43.66830163
Coefficient of variation (CV)-0.09681512862
Kurtosis-0.4098886122
Mean-451.0483254
Median Absolute Deviation (MAD)30.60205495
Skewness-0.3535951228
Sum-3158691.423
Variance1906.920568
MonotonicityNot monotonic
2022-11-20T22:31:29.232831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-413.38770669
 
0.1%
-426.00568959
 
0.1%
-417.97594529
 
0.1%
-443.34112168
 
0.1%
-399.89049678
 
0.1%
-398.2198557
 
0.1%
-418.69084327
 
0.1%
-413.6296127
 
0.1%
-406.77667257
 
0.1%
-426.07209647
 
0.1%
Other values (4750)6925
98.9%
ValueCountFrequency (%)
-576.55582591
< 0.1%
-575.92128321
< 0.1%
-574.58160581
< 0.1%
-573.74782581
< 0.1%
-573.51806411
< 0.1%
-572.99578321
< 0.1%
-571.75610831
< 0.1%
-571.50790481
< 0.1%
-569.98939991
< 0.1%
-568.98507171
< 0.1%
ValueCountFrequency (%)
-343.8395881
< 0.1%
-344.59588521
< 0.1%
-350.71042111
< 0.1%
-354.42331041
< 0.1%
-354.9876612
< 0.1%
-355.24272671
< 0.1%
-355.52447042
< 0.1%
-356.07402441
< 0.1%
-356.60951541
< 0.1%
-356.95723541
< 0.1%

Tuna
Real number (ℝ)

HIGH CORRELATION

Distinct6856
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-58391.13502
Minimum-96305.80639
Maximum-0.4280253306
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:29.278810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-96305.80639
5-th percentile-96035.83313
Q1-87535.44695
median-64779.3143
Q3-30984.87951
95-th percentile-5025.305122
Maximum-0.4280253306
Range96305.37837
Interquartile range (IQR)56550.56744

Descriptive statistics

Standard deviation30977.22412
Coefficient of variation (CV)-0.5305124505
Kurtosis-1.231503191
Mean-58391.13502
Median Absolute Deviation (MAD)26116.66634
Skewness0.3899861902
Sum-408913118.5
Variance959588414.5
MonotonicityNot monotonic
2022-11-20T22:31:29.324923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-30274.770244
 
0.1%
-11041.128753
 
< 0.1%
-87693.411213
 
< 0.1%
-27831.898653
 
< 0.1%
-48969.034223
 
< 0.1%
-86617.274443
 
< 0.1%
-95479.709932
 
< 0.1%
-55655.544222
 
< 0.1%
-66716.348682
 
< 0.1%
-96246.510762
 
< 0.1%
Other values (6846)6976
99.6%
ValueCountFrequency (%)
-96305.806391
< 0.1%
-96305.801541
< 0.1%
-96305.785321
< 0.1%
-96305.779911
< 0.1%
-96305.735721
< 0.1%
-96305.707291
< 0.1%
-96305.704881
< 0.1%
-96305.697271
< 0.1%
-96305.64141
< 0.1%
-96305.606082
< 0.1%
ValueCountFrequency (%)
-0.42802533061
< 0.1%
-6.6342857371
< 0.1%
-20.758132911
< 0.1%
-47.077084111
< 0.1%
-53.495794771
< 0.1%
-55.20741481
< 0.1%
-123.65973671
< 0.1%
-132.85616161
< 0.1%
-151.88922571
< 0.1%
-183.32173491
< 0.1%

Turkey
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6826
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean827432.0826
Minimum0
Maximum38582950.59
Zeros24
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2022-11-20T22:31:29.371507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30543.47651
Q176033.34881
median176642.0788
Q3627998.9261
95-th percentile4029873.164
Maximum38582950.59
Range38582950.59
Interquartile range (IQR)551965.5773

Descriptive statistics

Standard deviation1869694.489
Coefficient of variation (CV)2.259634994
Kurtosis64.57837272
Mean827432.0826
Median Absolute Deviation (MAD)123898.0972
Skewness6.132555525
Sum5794506875
Variance3.495757484 × 1012
MonotonicityNot monotonic
2022-11-20T22:31:29.416429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
0.3%
86541.076324
 
0.1%
59099.782263
 
< 0.1%
76326.238062
 
< 0.1%
239559.50542
 
< 0.1%
129498.95992
 
< 0.1%
76709.580962
 
< 0.1%
158767.67032
 
< 0.1%
1055212.1822
 
< 0.1%
319155.96852
 
< 0.1%
Other values (6816)6958
99.4%
ValueCountFrequency (%)
024
0.3%
2.347904164 × 10-51
 
< 0.1%
0.0042790553381
 
< 0.1%
0.0049364685041
 
< 0.1%
0.0063921690861
 
< 0.1%
0.0067854430331
 
< 0.1%
0.0089279055831
 
< 0.1%
0.0108531871
 
< 0.1%
0.011363856151
 
< 0.1%
0.023297079061
 
< 0.1%
ValueCountFrequency (%)
38582950.591
< 0.1%
30523668.331
< 0.1%
28542649.511
< 0.1%
23800387.831
< 0.1%
21311169.521
< 0.1%
21220850.491
< 0.1%
20942217.861
< 0.1%
18868793.71
< 0.1%
18552222.741
< 0.1%
17552580.211
< 0.1%

Turtle
Real number (ℝ)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.018656971 × 1017
Minimum-5.486601492 × 1018
Maximum-2.261745766 × 1010
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:29.457585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-5.486601492 × 1018
5-th percentile-1.384570922 × 1018
Q1-7.368776879 × 1016
median-6.74214822 × 1013
Q3-4.542837596 × 1011
95-th percentile-6.148062208 × 1010
Maximum-2.261745766 × 1010
Range5.486601469 × 1018
Interquartile range (IQR)7.368731451 × 1016

Descriptive statistics

Standard deviation6.117381879 × 1017
Coefficient of variation (CV)-3.030421694
Kurtosis27.92922476
Mean-2.018656971 × 1017
Median Absolute Deviation (MAD)6.739886474 × 1013
Skewness-4.856977482
Sum-1.413665477 × 1021
Variance3.742236106 × 1035
MonotonicityNot monotonic
2022-11-20T22:31:29.490264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
-6.148062208 × 1010625
 
8.9%
-1.671216579 × 1011615
 
8.8%
-1.991406735 × 1017529
 
7.6%
-4.542837596 × 1011515
 
7.4%
-5.327236818 × 1017510
 
7.3%
-6.74214822 × 1013420
 
6.0%
-7.368776879 × 1016404
 
5.8%
-2.480302536 × 1013382
 
5.5%
-1.832696225 × 1014368
 
5.3%
-9.124529627 × 1012343
 
4.9%
Other values (11)2292
32.7%
ValueCountFrequency (%)
-5.486601492 × 101822
 
0.3%
-3.294285015 × 1018137
 
2.0%
-1.384570922 × 1018307
4.4%
-5.327236818 × 1017510
7.3%
-1.991406735 × 1017529
7.6%
-7.368776879 × 1016404
5.8%
-2.716613994 × 1016274
3.9%
-1.000170364 × 1016138
 
2.0%
-3.680482074 × 1015122
 
1.7%
-1.35411727 × 1015197
 
2.8%
ValueCountFrequency (%)
-2.261745766 × 1010290
4.1%
-6.148062208 × 1010625
8.9%
-1.671216579 × 1011615
8.8%
-4.542837596 × 1011515
7.4%
-1.234871246 × 1012284
4.1%
-3.356727743 × 1012268
3.8%
-9.124529627 × 1012343
4.9%
-2.480302536 × 1013382
5.5%
-6.74214822 × 1013420
6.0%
-1.832696225 × 1014368
5.3%

Tyrannosaurus
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7000
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.08729577
Minimum47.83071034
Maximum66.12546919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2022-11-20T22:31:29.651907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum47.83071034
5-th percentile55.2470985
Q160.18788052
median62.99537067
Q364.76388928
95-th percentile65.88811202
Maximum66.12546919
Range18.29475885
Interquartile range (IQR)4.576008761

Descriptive statistics

Standard deviation3.381235829
Coefficient of variation (CV)0.05445938316
Kurtosis0.8977169285
Mean62.08729577
Median Absolute Deviation (MAD)2.07240636
Skewness-1.120339289
Sum434797.3323
Variance11.43275573
MonotonicityNot monotonic
2022-11-20T22:31:29.695393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.6813784
 
0.1%
53.014633611
 
< 0.1%
66.10525131
 
< 0.1%
64.899546321
 
< 0.1%
65.594817381
 
< 0.1%
64.855621961
 
< 0.1%
62.173299491
 
< 0.1%
61.22343641
 
< 0.1%
65.904640711
 
< 0.1%
65.297013291
 
< 0.1%
Other values (6990)6990
99.8%
ValueCountFrequency (%)
47.830710341
< 0.1%
48.358881341
< 0.1%
48.542369561
< 0.1%
48.57842561
< 0.1%
48.908067871
< 0.1%
48.984849231
< 0.1%
49.017430631
< 0.1%
49.02439151
< 0.1%
49.245701481
< 0.1%
49.317801731
< 0.1%
ValueCountFrequency (%)
66.125469191
< 0.1%
66.124101461
< 0.1%
66.123696841
< 0.1%
66.122871751
< 0.1%
66.122747621
< 0.1%
66.12075831
< 0.1%
66.120096361
< 0.1%
66.120091771
< 0.1%
66.119741751
< 0.1%
66.11969451
< 0.1%

Urial
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
e
2588 
a
1310 
d
1151 
c
1040 
b
914 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowe
2nd rowb
3rd rowe
4th rowd
5th rowa

Common Values

ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Length

2022-11-20T22:31:29.736947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:29.774926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Most occurring characters

ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e2588
37.0%
a1310
18.7%
d1151
16.4%
c1040
14.9%
b914
 
13.1%

Vicuna
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing7003
Missing (%)100.0%
Memory size54.8 KiB

Viper
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6826
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16886.36903
Minimum0
Maximum787407.155
Zeros24
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2022-11-20T22:31:29.816351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile623.3362553
Q11551.700996
median3604.940383
Q312816.30461
95-th percentile82242.30946
Maximum787407.155
Range787407.155
Interquartile range (IQR)11264.60362

Descriptive statistics

Standard deviation38157.0304
Coefficient of variation (CV)2.259634994
Kurtosis64.57837272
Mean16886.36903
Median Absolute Deviation (MAD)2528.532597
Skewness6.132555525
Sum118255242.3
Variance1455958969
MonotonicityNot monotonic
2022-11-20T22:31:29.861247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024
 
0.3%
1766.1444154
 
0.1%
1206.1180053
 
< 0.1%
1557.6783282
 
< 0.1%
4888.9694982
 
< 0.1%
2642.8359162
 
< 0.1%
1565.5016522
 
< 0.1%
3240.1565362
 
< 0.1%
21534.942492
 
< 0.1%
6513.3871132
 
< 0.1%
Other values (6816)6958
99.4%
ValueCountFrequency (%)
024
0.3%
4.79164115 × 10-71
 
< 0.1%
8.732765997 × 10-51
 
< 0.1%
0.00010074425521
 
< 0.1%
0.00013045243031
 
< 0.1%
0.00013847842921
 
< 0.1%
0.00018220215471
 
< 0.1%
0.00022149361221
 
< 0.1%
0.00023191543171
 
< 0.1%
0.00047545059321
 
< 0.1%
ValueCountFrequency (%)
787407.1551
< 0.1%
622932.00681
< 0.1%
582503.05121
< 0.1%
485722.20061
< 0.1%
434921.8271
< 0.1%
433078.58151
< 0.1%
427392.20121
< 0.1%
385077.42231
< 0.1%
378616.79061
< 0.1%
358215.92261
< 0.1%

Vole
Real number (ℝ)

HIGH CORRELATION

Distinct6877
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5367535.994
Minimum-74350381.82
Maximum165486775.4
Zeros11
Zeros (%)0.2%
Negative3477
Negative (%)49.7%
Memory size54.8 KiB
2022-11-20T22:31:29.907256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-74350381.82
5-th percentile-18742089.97
Q1-5831817.552
median38728.61358
Q310102346.42
95-th percentile51116793.78
Maximum165486775.4
Range239837157.2
Interquartile range (IQR)15934163.97

Descriptive statistics

Standard deviation21894525.06
Coefficient of variation (CV)4.079064414
Kurtosis5.313459376
Mean5367535.994
Median Absolute Deviation (MAD)7085107.809
Skewness1.689204244
Sum3.758885457 × 1010
Variance4.793702278 × 1014
MonotonicityNot monotonic
2022-11-20T22:31:29.952618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.2%
-232159.91524
 
0.1%
28943295.852
 
< 0.1%
-4452764.742
 
< 0.1%
61941889.982
 
< 0.1%
69363486.42
 
< 0.1%
-4228285.6192
 
< 0.1%
-8218504.0882
 
< 0.1%
-31983447.392
 
< 0.1%
62353790.832
 
< 0.1%
Other values (6867)6972
99.6%
ValueCountFrequency (%)
-74350381.821
< 0.1%
-71601409.161
< 0.1%
-70503905.441
< 0.1%
-67678725.161
< 0.1%
-67621401.121
< 0.1%
-67472651.421
< 0.1%
-67158901.991
< 0.1%
-66162642.681
< 0.1%
-65894746.211
< 0.1%
-65305371.381
< 0.1%
ValueCountFrequency (%)
165486775.41
< 0.1%
161803728.81
< 0.1%
131790971.91
< 0.1%
131420966.61
< 0.1%
130223617.51
< 0.1%
1258255051
< 0.1%
123585971.31
< 0.1%
119639350.21
< 0.1%
119358021.71
< 0.1%
116007557.61
< 0.1%

Vulture
Categorical

HIGH CORRELATION

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size403.6 KiB
b2
532 
k1
529 
e2
508 
z2
502 
x2
490 
Other values (35)
4442 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14006
Distinct characters28
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowd1
2nd rowe2
3rd rowr1
4th rowf2
5th rowa1

Common Values

ValueCountFrequency (%)
b2532
 
7.6%
k1529
 
7.6%
e2508
 
7.3%
z2502
 
7.2%
x2490
 
7.0%
d1467
 
6.7%
p1436
 
6.2%
c1403
 
5.8%
o1376
 
5.4%
n2331
 
4.7%
Other values (30)2429
34.7%

Length

2022-11-20T22:31:29.992653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b2532
 
7.6%
k1529
 
7.6%
e2508
 
7.3%
z2502
 
7.2%
x2490
 
7.0%
d1467
 
6.7%
p1436
 
6.2%
c1403
 
5.8%
o1376
 
5.4%
n2331
 
4.7%
Other values (30)2429
34.7%

Most occurring characters

ValueCountFrequency (%)
13828
27.3%
23175
22.7%
b683
 
4.9%
k529
 
3.8%
z513
 
3.7%
x511
 
3.6%
e509
 
3.6%
o492
 
3.5%
d470
 
3.4%
p436
 
3.1%
Other values (18)2860
20.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7003
50.0%
Lowercase Letter7003
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b683
 
9.8%
k529
 
7.6%
z513
 
7.3%
x511
 
7.3%
e509
 
7.3%
o492
 
7.0%
d470
 
6.7%
p436
 
6.2%
c403
 
5.8%
n332
 
4.7%
Other values (16)2125
30.3%
Decimal Number
ValueCountFrequency (%)
13828
54.7%
23175
45.3%

Most occurring scripts

ValueCountFrequency (%)
Common7003
50.0%
Latin7003
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b683
 
9.8%
k529
 
7.6%
z513
 
7.3%
x511
 
7.3%
e509
 
7.3%
o492
 
7.0%
d470
 
6.7%
p436
 
6.2%
c403
 
5.8%
n332
 
4.7%
Other values (16)2125
30.3%
Common
ValueCountFrequency (%)
13828
54.7%
23175
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII14006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13828
27.3%
23175
22.7%
b683
 
4.9%
k529
 
3.8%
z513
 
3.7%
x511
 
3.6%
e509
 
3.6%
o492
 
3.5%
d470
 
3.4%
p436
 
3.1%
Other values (18)2860
20.4%

Wallaby
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct3449
Distinct (%)99.4%
Missing3532
Missing (%)50.4%
Infinite0
Infinite (%)0.0%
Mean-12550971.78
Minimum-23084783.06
Maximum42283887.47
Zeros0
Zeros (%)0.0%
Negative3306
Negative (%)47.2%
Memory size54.8 KiB
2022-11-20T22:31:30.031259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-23084783.06
5-th percentile-20052633.43
Q1-17334379.37
median-14205950.68
Q3-9509895.185
95-th percentile-397374.4892
Maximum42283887.47
Range65368670.53
Interquartile range (IQR)7824484.182

Descriptive statistics

Standard deviation6940074.575
Coefficient of variation (CV)-0.5529511737
Kurtosis6.974251907
Mean-12550971.78
Median Absolute Deviation (MAD)3564963.103
Skewness1.991582366
Sum-4.356442304 × 1010
Variance4.816463511 × 1013
MonotonicityNot monotonic
2022-11-20T22:31:30.075946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6641426.4674
 
0.1%
-12534097.342
 
< 0.1%
-12062343.832
 
< 0.1%
-20317939.892
 
< 0.1%
-16993188.242
 
< 0.1%
-14273912.862
 
< 0.1%
-15259888.512
 
< 0.1%
-17943968.052
 
< 0.1%
-14710272.092
 
< 0.1%
-17500460.062
 
< 0.1%
Other values (3439)3449
49.3%
(Missing)3532
50.4%
ValueCountFrequency (%)
-23084783.061
< 0.1%
-23069954.411
< 0.1%
-23059624.91
< 0.1%
-23037886.991
< 0.1%
-23029146.51
< 0.1%
-22897571.851
< 0.1%
-22848220.421
< 0.1%
-22841024.581
< 0.1%
-22828095.361
< 0.1%
-22824629.581
< 0.1%
ValueCountFrequency (%)
42283887.471
< 0.1%
35629574.851
< 0.1%
34449985.461
< 0.1%
32373634.421
< 0.1%
31790005.971
< 0.1%
28314008.481
< 0.1%
26144456.491
< 0.1%
24842987.431
< 0.1%
24790314.581
< 0.1%
24237749.011
< 0.1%

Walrus
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6877
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62846.07663
Minimum2.265400964
Maximum336440.9097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2022-11-20T22:31:30.122762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.265400964
5-th percentile6492.494364
Q127760.69877
median53274.63301
Q384303.85081
95-th percentile159910.0169
Maximum336440.9097
Range336438.6443
Interquartile range (IQR)56543.15204

Descriptive statistics

Standard deviation47060.97835
Coefficient of variation (CV)0.748829217
Kurtosis1.617402505
Mean62846.07663
Median Absolute Deviation (MAD)27633.83655
Skewness1.215097922
Sum440111074.6
Variance2214735684
MonotonicityNot monotonic
2022-11-20T22:31:30.166943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9069.6685994
 
0.1%
70633.534572
 
< 0.1%
32183.99742
 
< 0.1%
158476.92232
 
< 0.1%
62819.864592
 
< 0.1%
19543.058562
 
< 0.1%
69759.885752
 
< 0.1%
44663.350022
 
< 0.1%
108990.72142
 
< 0.1%
53957.240572
 
< 0.1%
Other values (6867)6981
99.7%
ValueCountFrequency (%)
2.2654009641
< 0.1%
27.283288621
< 0.1%
27.841631331
< 0.1%
29.134724761
< 0.1%
87.662216321
< 0.1%
243.83390141
< 0.1%
275.54023491
< 0.1%
317.83406691
< 0.1%
381.74114261
< 0.1%
528.53376041
< 0.1%
ValueCountFrequency (%)
336440.90971
< 0.1%
297924.11081
< 0.1%
278454.82291
< 0.1%
277707.2871
< 0.1%
274845.91391
< 0.1%
272952.49041
< 0.1%
263728.75731
< 0.1%
263676.20971
< 0.1%
262015.23011
< 0.1%
257548.55721
< 0.1%

Wasp
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct7003
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7078336774
Minimum-1.800730204
Maximum11.17968731
Zeros0
Zeros (%)0.0%
Negative2926
Negative (%)41.8%
Memory size54.8 KiB
2022-11-20T22:31:30.213196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.800730204
5-th percentile-1.29762272
Q1-0.5272874034
median0.2672950766
Q31.370765816
95-th percentile4.570678926
Maximum11.17968731
Range12.98041751
Interquartile range (IQR)1.898053219

Descriptive statistics

Standard deviation1.801766681
Coefficient of variation (CV)2.545466171
Kurtosis2.902223282
Mean0.7078336774
Median Absolute Deviation (MAD)0.9059089282
Skewness1.561865994
Sum4956.959243
Variance3.246363171
MonotonicityNot monotonic
2022-11-20T22:31:30.258182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.3029171761
 
< 0.1%
2.8358950511
 
< 0.1%
0.79029733671
 
< 0.1%
-0.99057700381
 
< 0.1%
0.7379045931
 
< 0.1%
0.46214535441
 
< 0.1%
2.3327756881
 
< 0.1%
2.3162863271
 
< 0.1%
1.23669191
 
< 0.1%
0.40272040951
 
< 0.1%
Other values (6993)6993
99.9%
ValueCountFrequency (%)
-1.8007302041
< 0.1%
-1.8006771871
< 0.1%
-1.7995382161
< 0.1%
-1.7989719261
< 0.1%
-1.7964872821
< 0.1%
-1.7895869071
< 0.1%
-1.7871812171
< 0.1%
-1.7831806431
< 0.1%
-1.7818705081
< 0.1%
-1.7795949131
< 0.1%
ValueCountFrequency (%)
11.179687311
< 0.1%
10.831974491
< 0.1%
10.179900561
< 0.1%
10.108475981
< 0.1%
9.3595265151
< 0.1%
9.192611091
< 0.1%
8.8815206421
< 0.1%
8.8564680561
< 0.1%
8.789358331
< 0.1%
8.7614423771
< 0.1%

Warbler
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct83
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size403.6 KiB
h4
 
366
g1
 
333
o3
 
309
b1
 
287
e3
 
262
Other values (78)
5446 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14006
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.2%

Sample

1st rowf1
2nd rowo4
3rd rowg3
4th rowm3
5th rowh1

Common Values

ValueCountFrequency (%)
h4366
 
5.2%
g1333
 
4.8%
o3309
 
4.4%
b1287
 
4.1%
e3262
 
3.7%
y2261
 
3.7%
p1243
 
3.5%
t4241
 
3.4%
b3223
 
3.2%
h1222
 
3.2%
Other values (73)4256
60.8%

Length

2022-11-20T22:31:30.298531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h4366
 
5.2%
g1333
 
4.8%
o3309
 
4.4%
b1287
 
4.1%
e3262
 
3.7%
y2261
 
3.7%
p1243
 
3.5%
t4241
 
3.4%
b3223
 
3.2%
h1222
 
3.2%
Other values (73)4256
60.8%

Most occurring characters

ValueCountFrequency (%)
42322
16.6%
32036
14.5%
11417
 
10.1%
21228
 
8.8%
h788
 
5.6%
g720
 
5.1%
b559
 
4.0%
y506
 
3.6%
o468
 
3.3%
p400
 
2.9%
Other values (20)3562
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7003
50.0%
Lowercase Letter7003
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h788
 
11.3%
g720
 
10.3%
b559
 
8.0%
y506
 
7.2%
o468
 
6.7%
p400
 
5.7%
r332
 
4.7%
f328
 
4.7%
e279
 
4.0%
l258
 
3.7%
Other values (16)2365
33.8%
Decimal Number
ValueCountFrequency (%)
42322
33.2%
32036
29.1%
11417
20.2%
21228
17.5%

Most occurring scripts

ValueCountFrequency (%)
Common7003
50.0%
Latin7003
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
h788
 
11.3%
g720
 
10.3%
b559
 
8.0%
y506
 
7.2%
o468
 
6.7%
p400
 
5.7%
r332
 
4.7%
f328
 
4.7%
e279
 
4.0%
l258
 
3.7%
Other values (16)2365
33.8%
Common
ValueCountFrequency (%)
42322
33.2%
32036
29.1%
11417
20.2%
21228
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII14006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42322
16.6%
32036
14.5%
11417
 
10.1%
21228
 
8.8%
h788
 
5.6%
g720
 
5.1%
b559
 
4.0%
y506
 
3.6%
o468
 
3.3%
p400
 
2.9%
Other values (20)3562
25.4%

Weasel
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
b
3734 
a
3269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowa
2nd rowb
3rd rowa
4th rowa
5th rowa

Common Values

ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Length

2022-11-20T22:31:30.331605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:30.366150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Most occurring characters

ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b3734
53.3%
a3269
46.7%

Whale
Real number (ℝ)

HIGH CORRELATION

Distinct6872
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean737082.6371
Minimum-10129928.05
Maximum47837674.79
Zeros9
Zeros (%)0.1%
Negative4207
Negative (%)60.1%
Memory size54.8 KiB
2022-11-20T22:31:30.400583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-10129928.05
5-th percentile-6621011.615
Q1-2662170.473
median-1046974.69
Q31889262.461
95-th percentile14795591.19
Maximum47837674.79
Range57967602.84
Interquartile range (IQR)4551432.933

Descriptive statistics

Standard deviation6550392.039
Coefficient of variation (CV)8.886916757
Kurtosis6.393386018
Mean737082.6371
Median Absolute Deviation (MAD)2288749
Skewness2.117882399
Sum5161789707
Variance4.290763586 × 1013
MonotonicityNot monotonic
2022-11-20T22:31:30.443824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
0.1%
-1605557.6924
 
0.1%
-1587713.5662
 
< 0.1%
488336.8752
 
< 0.1%
-922033.85352
 
< 0.1%
-4162045.5242
 
< 0.1%
1452780.0512
 
< 0.1%
1203472.522
 
< 0.1%
802210.68582
 
< 0.1%
12061046.852
 
< 0.1%
Other values (6862)6974
99.6%
ValueCountFrequency (%)
-10129928.051
< 0.1%
-10121498.061
< 0.1%
-10110251.081
< 0.1%
-10104371.681
< 0.1%
-10084815.81
< 0.1%
-10055799.011
< 0.1%
-10053527.491
< 0.1%
-10048737.081
< 0.1%
-9872301.4881
< 0.1%
-9857062.681
< 0.1%
ValueCountFrequency (%)
47837674.791
< 0.1%
45128843.011
< 0.1%
41013383.481
< 0.1%
40253034.131
< 0.1%
39183893.21
< 0.1%
39157056.091
< 0.1%
38147910.531
< 0.1%
38127479.71
< 0.1%
380516211
< 0.1%
37907302.131
< 0.1%

Whippet
Real number (ℝ)

HIGH CORRELATION

Distinct6713
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.296472561
Minimum-1.342072154
Maximum-0.02861438727
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:30.489509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.342072154
5-th percentile-1.342072154
Q1-1.342071913
median-1.341861136
Q3-1.328099895
95-th percentile-1.053891705
Maximum-0.02861438727
Range1.313457767
Interquartile range (IQR)0.01397201767

Descriptive statistics

Standard deviation0.1354538647
Coefficient of variation (CV)-0.1044787748
Kurtosis24.43476869
Mean-1.296472561
Median Absolute Deviation (MAD)0.0002110186007
Skewness4.572912007
Sum-9079.197341
Variance0.01834774945
MonotonicityNot monotonic
2022-11-20T22:31:30.536123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.342072154200
 
2.9%
-1.34207215426
 
0.4%
-1.34207215417
 
0.2%
-1.3420721546
 
0.1%
-1.3420721546
 
0.1%
-1.3420721544
 
0.1%
-1.2332575794
 
0.1%
-1.3420721543
 
< 0.1%
-1.3420721543
 
< 0.1%
-1.3420721543
 
< 0.1%
Other values (6703)6731
96.1%
ValueCountFrequency (%)
-1.342072154200
2.9%
-1.34207215417
 
0.2%
-1.34207215426
 
0.4%
-1.3420721546
 
0.1%
-1.3420721542
 
< 0.1%
-1.3420721546
 
0.1%
-1.3420721543
 
< 0.1%
-1.3420721541
 
< 0.1%
-1.3420721543
 
< 0.1%
-1.3420721544
 
0.1%
ValueCountFrequency (%)
-0.028614387271
< 0.1%
-0.071281209441
< 0.1%
-0.109409191
< 0.1%
-0.131260711
< 0.1%
-0.14995183921
< 0.1%
-0.15965374551
< 0.1%
-0.16997351821
< 0.1%
-0.17580866781
< 0.1%
-0.21727141471
< 0.1%
-0.23762036621
< 0.1%

Whitefish
Real number (ℝ)

HIGH CORRELATION

Distinct7000
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-35364378.63
Minimum-35366648.72
Maximum-35358161.86
Zeros0
Zeros (%)0.0%
Negative7003
Negative (%)100.0%
Memory size54.8 KiB
2022-11-20T22:31:30.583526image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-35366648.72
5-th percentile-35365933.2
Q1-35365191.15
median-35364515.66
Q3-35363731.76
95-th percentile-35362339.42
Maximum-35358161.86
Range8486.858636
Interquartile range (IQR)1459.392734

Descriptive statistics

Standard deviation1117.262028
Coefficient of variation (CV)-3.159286467 × 10-5
Kurtosis0.9946407857
Mean-35364378.63
Median Absolute Deviation (MAD)720.8858385
Skewness0.8070279469
Sum-2.476567435 × 1011
Variance1248274.439
MonotonicityNot monotonic
2022-11-20T22:31:30.627544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-35365660.124
 
0.1%
-35365779.461
 
< 0.1%
-35364480.531
 
< 0.1%
-35364273.811
 
< 0.1%
-35365338.051
 
< 0.1%
-35365055.431
 
< 0.1%
-35363124.091
 
< 0.1%
-35364270.091
 
< 0.1%
-35364074.261
 
< 0.1%
-35364472.041
 
< 0.1%
Other values (6990)6990
99.8%
ValueCountFrequency (%)
-35366648.721
< 0.1%
-35366646.051
< 0.1%
-35366639.551
< 0.1%
-35366633.51
< 0.1%
-35366594.241
< 0.1%
-35366560.571
< 0.1%
-35366557.81
< 0.1%
-35366552.931
< 0.1%
-35366547.821
< 0.1%
-35366542.181
< 0.1%
ValueCountFrequency (%)
-35358161.861
< 0.1%
-35358926.011
< 0.1%
-35359071.481
< 0.1%
-35359189.091
< 0.1%
-35359297.511
< 0.1%
-35359334.31
< 0.1%
-35359518.011
< 0.1%
-35359699.451
< 0.1%
-35359710.621
< 0.1%
-35359771.791
< 0.1%

Wildcat
Real number (ℝ)

HIGH CORRELATION

Distinct7000
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean603.0493801
Minimum-446.9401989
Maximum17809.14917
Zeros0
Zeros (%)0.0%
Negative3104
Negative (%)44.3%
Memory size54.8 KiB
2022-11-20T22:31:30.672139image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-446.9401989
5-th percentile-354.6819189
Q1-187.2316671
median72.51596863
Q3769.9422742
95-th percentile3293.833624
Maximum17809.14917
Range18256.08937
Interquartile range (IQR)957.1739414

Descriptive statistics

Standard deviation1485.652033
Coefficient of variation (CV)2.463566139
Kurtosis21.25714668
Mean603.0493801
Median Absolute Deviation (MAD)335.2288557
Skewness3.819586873
Sum4223154.809
Variance2207161.962
MonotonicityNot monotonic
2022-11-20T22:31:30.714785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
808.93541084
 
0.1%
-395.15900981
 
< 0.1%
88.821021591
 
< 0.1%
291.60461461
 
< 0.1%
-133.6348441
 
< 0.1%
204.45896331
 
< 0.1%
-217.65516131
 
< 0.1%
-287.10965981
 
< 0.1%
1918.5862971
 
< 0.1%
-199.61475341
 
< 0.1%
Other values (6990)6990
99.8%
ValueCountFrequency (%)
-446.94019891
< 0.1%
-442.4276011
< 0.1%
-441.65886491
< 0.1%
-440.39162071
< 0.1%
-439.46096161
< 0.1%
-434.86645251
< 0.1%
-434.69325861
< 0.1%
-433.20010731
< 0.1%
-432.95673751
< 0.1%
-432.24000911
< 0.1%
ValueCountFrequency (%)
17809.149171
< 0.1%
16989.107211
< 0.1%
14033.835961
< 0.1%
13933.478921
< 0.1%
13802.867031
< 0.1%
13642.401181
< 0.1%
13307.812821
< 0.1%
13004.146321
< 0.1%
12838.309971
< 0.1%
12659.369081
< 0.1%

Wildebeest
Real number (ℝ)

HIGH CORRELATION

Distinct7000
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05190221249
Minimum-0.6624905993
Maximum2.051849613
Zeros0
Zeros (%)0.0%
Negative3487
Negative (%)49.8%
Memory size54.8 KiB
2022-11-20T22:31:30.759892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-0.6624905993
5-th percentile-0.450524397
Q1-0.2205198919
median0.002617042481
Q30.2722469582
95-th percentile0.7117966175
Maximum2.051849613
Range2.714340212
Interquartile range (IQR)0.49276685

Descriptive statistics

Standard deviation0.3655973415
Coefficient of variation (CV)7.043964486
Kurtosis0.8729477583
Mean0.05190221249
Median Absolute Deviation (MAD)0.2401871422
Skewness0.7937253384
Sum363.471194
Variance0.1336614161
MonotonicityNot monotonic
2022-11-20T22:31:30.804773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.39762890274
 
0.1%
0.021951513971
 
< 0.1%
-0.12254840221
 
< 0.1%
0.059824323991
 
< 0.1%
0.1502277931
 
< 0.1%
-0.070166152831
 
< 0.1%
0.10878035811
 
< 0.1%
0.11538564371
 
< 0.1%
-0.092845935511
 
< 0.1%
-0.30847793471
 
< 0.1%
Other values (6990)6990
99.8%
ValueCountFrequency (%)
-0.66249059931
< 0.1%
-0.65361040111
< 0.1%
-0.65226578661
< 0.1%
-0.65034629371
< 0.1%
-0.64895464191
< 0.1%
-0.64847474241
< 0.1%
-0.64517130461
< 0.1%
-0.64389121411
< 0.1%
-0.63825244851
< 0.1%
-0.63625924731
< 0.1%
ValueCountFrequency (%)
2.0518496131
< 0.1%
1.9262482491
< 0.1%
1.8640180171
< 0.1%
1.7370802741
< 0.1%
1.6639428921
< 0.1%
1.6336280191
< 0.1%
1.6273872541
< 0.1%
1.625872591
< 0.1%
1.5803611431
< 0.1%
1.5593780051
< 0.1%

Wildfowl
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
b
6544 
y
 
286
m
 
104
f
 
34
w
 
13
Other values (5)
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowm
5th rowb

Common Values

ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Length

2022-11-20T22:31:30.965784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:31.006021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b6544
93.4%
y286
 
4.1%
m104
 
1.5%
f34
 
0.5%
w13
 
0.2%
l12
 
0.2%
v7
 
0.1%
c1
 
< 0.1%
u1
 
< 0.1%
z1
 
< 0.1%

Wolf
Categorical

HIGH CORRELATION

Distinct28
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size403.6 KiB
k1
1913 
z2
1321 
s1
1236 
r2
615 
m1
565 
Other values (23)
1353 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters14006
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowk1
2nd rowm1
3rd rows2
4th rowk1
5th rowj1

Common Values

ValueCountFrequency (%)
k11913
27.3%
z21321
18.9%
s11236
17.6%
r2615
 
8.8%
m1565
 
8.1%
s2337
 
4.8%
r1189
 
2.7%
e2166
 
2.4%
w1148
 
2.1%
c188
 
1.3%
Other values (18)425
 
6.1%

Length

2022-11-20T22:31:31.042932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
k11913
27.3%
z21321
18.9%
s11236
17.6%
r2615
 
8.8%
m1565
 
8.1%
s2337
 
4.8%
r1189
 
2.7%
e2166
 
2.4%
w1148
 
2.1%
c188
 
1.3%
Other values (18)425
 
6.1%

Most occurring characters

ValueCountFrequency (%)
14425
31.6%
22578
18.4%
k1927
13.8%
s1573
 
11.2%
z1321
 
9.4%
r804
 
5.7%
m565
 
4.0%
e166
 
1.2%
w148
 
1.1%
c99
 
0.7%
Other values (14)400
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7003
50.0%
Lowercase Letter7003
50.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k1927
27.5%
s1573
22.5%
z1321
18.9%
r804
11.5%
m565
 
8.1%
e166
 
2.4%
w148
 
2.1%
c99
 
1.4%
j84
 
1.2%
i66
 
0.9%
Other values (12)250
 
3.6%
Decimal Number
ValueCountFrequency (%)
14425
63.2%
22578
36.8%

Most occurring scripts

ValueCountFrequency (%)
Common7003
50.0%
Latin7003
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
k1927
27.5%
s1573
22.5%
z1321
18.9%
r804
11.5%
m565
 
8.1%
e166
 
2.4%
w148
 
2.1%
c99
 
1.4%
j84
 
1.2%
i66
 
0.9%
Other values (12)250
 
3.6%
Common
ValueCountFrequency (%)
14425
63.2%
22578
36.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII14006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14425
31.6%
22578
18.4%
k1927
13.8%
s1573
 
11.2%
z1321
 
9.4%
r804
 
5.7%
m565
 
4.0%
e166
 
1.2%
w148
 
1.1%
c99
 
0.7%
Other values (14)400
 
2.9%

Wolverine
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
m
1874 
x
1810 
s
872 
r
616 
h
521 
Other values (10)
1310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowm
2nd rowx
3rd rowr
4th roww
5th rows

Common Values

ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

Length

2022-11-20T22:31:31.075843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

Most occurring characters

ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7003
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7003
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m1874
26.8%
x1810
25.8%
s872
12.5%
r616
 
8.8%
h521
 
7.4%
l418
 
6.0%
n292
 
4.2%
y219
 
3.1%
z148
 
2.1%
d94
 
1.3%
Other values (5)139
 
2.0%

target
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size396.8 KiB
0
6003 
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7003
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Length

2022-11-20T22:31:31.108636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-20T22:31:31.142857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Most occurring characters

ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7003
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common7003
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII7003
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06003
85.7%
11000
 
14.3%

Interactions

2022-11-20T22:31:27.269185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.306947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.334354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.080253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.865005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.749068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.487010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.400628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.222867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.094402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.880725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.802576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.541263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.294133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.200032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.951240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.832550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.610898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.489281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.308389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.373257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.372996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.121674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.905104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.787696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.559154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.442926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.262447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.135976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.923112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.841464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.581190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.334943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.239174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.991080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.873544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.650434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.530911image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.346704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.476057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.411114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.161166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.066469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.825412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.598143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.506913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.300853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.176201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.964471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.879193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.619780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.374266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.277405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.029568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.912912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.688852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.570243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.387617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.545261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.450971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.203300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.107404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.865221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.640211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.550798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.342456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.218499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.008691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.918870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.660679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.540103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.317833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.070562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.955630image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.729743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.612030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.427224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.594949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.489643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.244249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.147186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.903221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.681037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.593762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.381275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.260056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.050748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.957237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.700346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.580624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.357313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.110344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.996372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.769692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.653248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.464423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:12.633270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.526755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-20T22:31:24.671555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.447016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.329779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.105166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.904871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.213665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.962624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.742742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.629592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.370833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.282276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.097331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.977036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.756984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.678701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.426044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.175896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.076953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.833897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.713230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.488801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.370920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.147579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:28.065902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.253242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.001076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.782590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.668909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.408977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.321133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.138121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.016075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.797617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.719069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.464360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.214331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.117350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.872779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.752175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.528670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.409847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.187787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:28.106299image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:13.294887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.041830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:14.824499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:15.710252image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:16.449111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:17.361517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:18.180791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.056153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:19.839962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:20.761822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:21.504026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:22.255201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.159824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:23.912861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:24.793375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:25.570788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:26.450391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-20T22:31:27.229204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-20T22:31:31.185469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-20T22:31:31.279047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-20T22:31:31.363890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-20T22:31:31.448965image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-20T22:31:31.527733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-20T22:31:31.597967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-20T22:31:28.194346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-20T22:31:28.432492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-20T22:31:28.519819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-20T22:31:28.562333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

TernThrushTickTigerTiglonToadTortoiseTroutTunaTurkeyTurtleTyrannosaurusUrialVicunaViperVoleVultureWallabyWalrusWaspWarblerWeaselWhaleWhippetWhitefishWildcatWildebeestWildfowlWolfWolverinetarget
0-253077.772390-49.659246-0.679765sNaNl-12.055165-463.332642-41860.4193223.256739e+05-4.542838e+1153.014634eNaN6646.405348-8.374344e+06d1-1.800251e+0781581.6086674.302917f1a-3.297065e+06-0.974495-3.536578e+07-395.1590100.021952bk1m1
1380192.123132-49.659246-1.356456sFalsel-30.235998-388.822813-94810.0299591.154421e+03-3.356728e+1261.011543bNaN23.559610-4.272011e+06e2NaN17260.087089-1.251457o4b-1.043690e+06-1.342061-3.536549e+07609.3321210.269383bm1x0
2-218627.876963-49.659246-0.542194sFalsel-10.443575-519.014942-46075.9316832.248867e+06-1.671217e+1159.257043eNaN45895.2463241.749547e+07r1NaN198039.108141-0.177949g3a4.892868e+06-1.335934-3.536456e+071175.5369020.341417bs2r0
3-178076.879466-49.659246-0.485974sNaNl-8.374563-504.614247-73554.3915051.452062e+07-1.832696e+1460.097613dNaN296339.1068739.290504e+07f2NaN32531.9481581.090384m3a2.814345e+07-1.073553-3.536497e+07-359.3207590.003191mk1w0
4-169250.190006-49.659246-0.600561sFalsel-10.269902-455.225099-41390.9906812.689196e+05-5.327237e+1755.450825aNaN5488.154809-4.480257e+06a1NaN6325.7740060.058029h1a-1.801627e+05-1.341853-3.536321e+07-346.856580-0.250332bj1s0
5-68161.820081-49.659246-0.882380sNaNl-16.547101-407.422566-43057.2458341.826905e+05-1.671217e+1165.893751aNaN3728.3779442.750597e+06e2-1.490572e+0741700.5330044.379026p1b-6.839139e+05-1.337109-3.536274e+07-115.853672-0.432992bk1x1
6-37585.923372-49.659246-0.353163sFalseu-7.958229-512.684067-23556.3543296.464157e+05-4.981713e+1464.706621eNaN13192.1563801.115918e+07x2-5.157540e+0638294.751898-0.124974p2a2.148650e+07-1.341532-3.536491e+07133.438248-0.204956yr2l0
7-15600.126197-49.659246-0.620241sFalses-11.309703-473.938277-70432.5991094.948221e+05-3.356728e+1256.530645dNaN10098.4099626.751724e+06z2-1.870867e+0798425.208068-0.180859r3a8.378739e+05-1.327982-3.536507e+07-12.314200-0.031765br2s0
8-199256.215512-49.659246-0.936732sNaNl-20.919942-448.731092-86327.0496341.191677e+05-1.384571e+1855.203216cNaN2431.994498-1.234349e+07c1NaN54168.589093-1.150958z4b-1.962629e+06-1.130040-3.536287e+072875.713393-0.130135bk1m0
9-240720.282794-49.659246-1.209454sNaNl-25.534579-409.084454-70362.4454983.089294e+04-3.294285e+1860.562780dNaN630.468235-6.874767e+06o1NaN61183.540730-0.670065w2b-1.631413e+06-1.342072-3.536261e+07141.2114700.082113bk1m0

Last rows

TernThrushTickTigerTiglonToadTortoiseTroutTunaTurkeyTurtleTyrannosaurusUrialVicunaViperVoleVultureWallabyWalrusWaspWarblerWeaselWhaleWhippetWhitefishWildcatWildebeestWildfowlWolfWolverinetarget
6993-369114.864874-49.659246-0.394964sFalses-7.185611-531.124535-60745.3425056.756377e+05-6.148062e+1061.051872dNaN13788.5237781.725753e+07r1NaN22956.9647031.661525g2a7.857435e+06-1.342072-3.536319e+07323.032709-0.248675bs1r0
6994-225906.631060-49.659246-1.271802sFalsel-27.916152-401.815729-26687.7464898.022072e+04-1.832696e+1465.382152eNaN1637.1575198.641346e+05p1-1.790219e+0734947.2969895.518953g1b1.770881e+05-1.312882-3.536418e+07-310.056445-0.050030bs2x1
699548818.062147-49.659246-0.963420sNaNl-17.970391-423.281560-8588.9626121.388766e+05-6.148062e+1061.547994eNaN2834.2161488.357713e+06k1NaN56103.2133288.565353g3b-1.630829e+06-1.340555-3.536501e+07-242.5805590.336831bs1m1
699685296.365146-49.659246-0.955535sNaNu-18.993543-455.751188-77862.5342657.862123e+05-1.384571e+1865.533336aNaN16045.1487211.428512e+07z1NaN96798.3405890.855201p2a1.180669e+07-1.342033-3.536494e+07127.5623940.444760yj1h0
6997-208503.4482490.000000-0.992914sNaNl-19.012847-457.164934-37875.9975719.083280e+05-1.234871e+1256.197450eNaN18537.3068371.349089e+07h2-1.996737e+07123248.3534090.009993l4a3.034118e+06-1.236485-3.536652e+07789.384950-0.438851bm1s0
6998-246840.700218-49.659246-1.180932sNaNl-25.887398-397.556639-93903.1834377.822274e+04-1.671217e+1161.087486bNaN1596.382399-4.080524e+05k1-8.266329e+0612976.8183250.743041p2b4.398786e+05-1.342072-3.536359e+07159.699665-0.294403br2x0
6999-32731.429095-49.659246-0.355033sNaNl-6.157721-504.683096-86026.1101613.389865e+06-4.542838e+1165.969527aNaN69180.9199076.171694e+07n2-5.615278e+0649758.375879-0.355619n3a1.224437e+07-1.342072-3.536138e+07163.732671-0.379221bs1z0
7000-73991.670435-49.659246-0.868682sNaNl-16.315083-463.735452-96302.5455309.704820e+04-2.716614e+1660.112278eNaN1980.5755022.000538e+06z2NaN67564.4675580.408306k3a2.453862e+06-1.305983-3.536540e+07-172.0334580.150357be2m0
7001-278624.327833-16.772943-0.675340sNaNu-11.704156-472.480093-41097.9315919.764238e+05-1.384571e+1862.482754aNaN19927.015801-2.102992e+07c1-7.363527e+06146034.872338-0.851745o3a-6.285754e+06-1.342015-3.536361e+07-67.920425-0.030202br2h0
7002332644.018190-49.659246-1.349087sFalsel-28.280392-364.077479-82717.8223747.286965e+04-5.327237e+1758.386166cNaN1487.135712-3.750265e+06e2NaN39829.4142681.854286g1b5.038175e+05-1.315773-3.536487e+0711.7815810.234827bz2x0